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Copy file name to clipboardExpand all lines: docs/dyn/aiplatform_v1.projects.locations.customJobs.html
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"scheduling": { # All parameters related to queuing and scheduling of custom jobs. # Scheduling options for a CustomJob.
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"disableRetries": True or False, # Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false.
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"maxWaitDuration": "A String", # Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
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"restartJobOnWorkerRestart": True or False, # Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
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"restartJobOnWorkerRestart": True or False, # Optional. Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
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"strategy": "A String", # Optional. This determines which type of scheduling strategy to use.
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"timeout": "A String", # The maximum job running time. The default is 7 days.
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"timeout": "A String", # Optional. The maximum job running time. The default is 7 days.
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},
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"serviceAccount": "A String", # Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the [Vertex AI Custom Code Service Agent](https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) for the CustomJob's project is used.
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"tensorboard": "A String", # Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
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"scheduling": { # All parameters related to queuing and scheduling of custom jobs. # Scheduling options for a CustomJob.
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"disableRetries": True or False, # Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false.
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"maxWaitDuration": "A String", # Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
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"restartJobOnWorkerRestart": True or False, # Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
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"restartJobOnWorkerRestart": True or False, # Optional. Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
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"strategy": "A String", # Optional. This determines which type of scheduling strategy to use.
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"timeout": "A String", # The maximum job running time. The default is 7 days.
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"timeout": "A String", # Optional. The maximum job running time. The default is 7 days.
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},
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"serviceAccount": "A String", # Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the [Vertex AI Custom Code Service Agent](https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) for the CustomJob's project is used.
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"tensorboard": "A String", # Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
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"scheduling": { # All parameters related to queuing and scheduling of custom jobs. # Scheduling options for a CustomJob.
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"disableRetries": True or False, # Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false.
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"maxWaitDuration": "A String", # Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
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"restartJobOnWorkerRestart": True or False, # Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
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"restartJobOnWorkerRestart": True or False, # Optional. Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
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"strategy": "A String", # Optional. This determines which type of scheduling strategy to use.
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"timeout": "A String", # The maximum job running time. The default is 7 days.
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"timeout": "A String", # Optional. The maximum job running time. The default is 7 days.
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},
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"serviceAccount": "A String", # Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the [Vertex AI Custom Code Service Agent](https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) for the CustomJob's project is used.
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"tensorboard": "A String", # Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
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"scheduling": { # All parameters related to queuing and scheduling of custom jobs. # Scheduling options for a CustomJob.
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"disableRetries": True or False, # Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides `Scheduling.restart_job_on_worker_restart` to false.
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"maxWaitDuration": "A String", # Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
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"restartJobOnWorkerRestart": True or False, # Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
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"restartJobOnWorkerRestart": True or False, # Optional. Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
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"strategy": "A String", # Optional. This determines which type of scheduling strategy to use.
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"timeout": "A String", # The maximum job running time. The default is 7 days.
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"timeout": "A String", # Optional. The maximum job running time. The default is 7 days.
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},
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"serviceAccount": "A String", # Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the [Vertex AI Custom Code Service Agent](https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) for the CustomJob's project is used.
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"tensorboard": "A String", # Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
Copy file name to clipboardExpand all lines: docs/dyn/aiplatform_v1.projects.locations.datasets.html
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{ # Request message for DatasetService.ExportData.
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"exportConfig": { # Describes what part of the Dataset is to be exported, the destination of the export and how to export. # Required. The desired output location.
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"annotationSchemaUri": "A String", # The Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only used for custom training data export use cases. Only applicable to Datasets that have DataItems and Annotations. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
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"annotationSchemaUri": "A String", # The Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 [Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with metadata of the Dataset specified by ExportDataRequest.name. Only used for custom training data export use cases. Only applicable to Datasets that have DataItems and Annotations. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
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"annotationsFilter": "A String", # An expression for filtering what part of the Dataset is to be exported. Only Annotations that match this filter will be exported. The filter syntax is the same as in ListAnnotations.
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"exportUse": "A String", # Indicates the usage of the exported files.
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"filterSplit": { # Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as '-' (the minus sign). Supported only for unstructured Datasets. # Split based on the provided filters for each set.
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"gcsDestination": { # The Google Cloud Storage location where the output is to be written to. # The Google Cloud Storage location where the output is to be written to. In the given directory a new directory will be created with name: `export-data--` where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory. Inside that directory, annotations with the same schema will be grouped into sub directories which are named with the corresponding annotations' schema title. Inside these sub directories, a schema.yaml will be created to describe the output format.
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"outputUriPrefix": "A String", # Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
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},
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"savedQueryId": "A String", # The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only used for custom training data export use cases. Only applicable to Datasets that have SavedQueries. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
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"savedQueryId": "A String", # The ID of a SavedQuery (annotation set) under the Dataset specified by ExportDataRequest.name used for filtering Annotations for training. Only used for custom training data export use cases. Only applicable to Datasets that have SavedQueries. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
Copy file name to clipboardExpand all lines: docs/dyn/aiplatform_v1.projects.locations.deploymentResourcePools.html
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},
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},
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},
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"fasterDeploymentConfig": { # Configuration for faster model deployment. # Configuration for faster model deployment.
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"fastTryoutEnabled": True or False, # If true, enable fast tryout feature for this deployed model.
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},
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"id": "A String", # Immutable. The ID of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are `/[0-9]/`.
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"model": "A String", # Required. The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: `projects/{project}/locations/{location}/models/{model}@2` or `projects/{project}/locations/{location}/models/{model}@golden` if no version is specified, the default version will be deployed.
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"modelVersionId": "A String", # Output only. The version ID of the model that is deployed.
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